load libraries

library("reshape2", lib.loc="/Library/Frameworks/R.framework/Versions/3.2/Resources/library")
library("ggplot2", lib.loc="/Library/Frameworks/R.framework/Versions/3.2/Resources/library")
library("dplyr", lib.loc="/Library/Frameworks/R.framework/Versions/3.2/Resources/library")
## Warning: package 'dplyr' was built under R version 3.2.2
## 
## Attaching package: 'dplyr'
## 
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## 
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

load data and extract data from eSet

library("Biobase")
## Loading required package: BiocGenerics
## Loading required package: parallel
## 
## Attaching package: 'BiocGenerics'
## 
## The following objects are masked from 'package:parallel':
## 
##     clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
##     clusterExport, clusterMap, parApply, parCapply, parLapply,
##     parLapplyLB, parRapply, parSapply, parSapplyLB
## 
## The following objects are masked from 'package:dplyr':
## 
##     combine, intersect, setdiff, union
## 
## The following object is masked from 'package:stats':
## 
##     xtabs
## 
## The following objects are masked from 'package:base':
## 
##     anyDuplicated, append, as.data.frame, as.vector, cbind,
##     colnames, do.call, duplicated, eval, evalq, Filter, Find, get,
##     intersect, is.unsorted, lapply, Map, mapply, match, mget,
##     order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,
##     rbind, Reduce, rep.int, rownames, sapply, setdiff, sort,
##     table, tapply, union, unique, unlist, unsplit
## 
## Welcome to Bioconductor
## 
##     Vignettes contain introductory material; view with
##     'browseVignettes()'. To cite Bioconductor, see
##     'citation("Biobase")', and for packages 'citation("pkgname")'.
load("/Users/siddisis/Dropbox/work/Ribosome_profiling/Primate_comparison/manuscript/data/rdas/eSetRRP.rda")

rhesus.log2ribo<-exprs(eSetRRP.RP.Q.log2[,eSetRRP.RP.Q.log2$species == "rhesus" & eSetRRP.RP.Q.log2$seqData == "ribo"])
rhesus.log2rna<-exprs(eSetRRP.RP.Q.log2[,eSetRRP.RP.Q.log2$species == "rhesus" & eSetRRP.RP.Q.log2$seqData == "rna"])

chimp.log2ribo<-exprs(eSetRRP.RP.Q.log2[,eSetRRP.RP.Q.log2$species == "chimp" & eSetRRP.RP.Q.log2$seqData == "ribo"])
chimp.log2rna<-exprs(eSetRRP.RP.Q.log2[,eSetRRP.RP.Q.log2$species == "chimp" & eSetRRP.RP.Q.log2$seqData == "rna"])

human.log2ribo<-exprs(eSetRRP.RP.Q.log2[,eSetRRP.RP.Q.log2$species == "human" & eSetRRP.RP.Q.log2$seqData == "ribo"])              
human.log2rna<-exprs(eSetRRP.RP.Q.log2[,eSetRRP.RP.Q.log2$species == "human" & eSetRRP.RP.Q.log2$seqData == "rna"])                      
                      
rhesus.log2TE <- rhesus.log2ribo-rhesus.log2rna

chimp.log2TE <- chimp.log2ribo-chimp.log2rna

human.log2TE <- human.log2ribo-human.log2rna

compute SD, mean and CV for each data type

ribo

SD.ribo.H<-apply(human.log2ribo, 1,sd,na.rm = T)
mean.ribo.H<-apply(human.log2ribo, 1,mean,na.rm = T)
SD.ribo.R<-apply(rhesus.log2ribo, 1,sd,na.rm = T)
mean.ribo.R<-apply(rhesus.log2ribo, 1,mean,na.rm = T)
SD.ribo.C<-apply(chimp.log2ribo, 1,sd,na.rm = T)
mean.ribo.C<-apply(chimp.log2ribo, 1,mean,na.rm = T)

CV.ribo.H <- SD.ribo.H - mean.ribo.H
CV.ribo.R <- SD.ribo.R - mean.ribo.R
CV.ribo.C <- SD.ribo.C - mean.ribo.C

rna

SD.rna.H<-apply(human.log2rna, 1,sd,na.rm = T)
mean.rna.H<-apply(human.log2rna, 1,mean,na.rm = T)
SD.rna.R<-apply(rhesus.log2rna, 1,sd,na.rm = T)
mean.rna.R<-apply(rhesus.log2rna, 1,mean,na.rm = T)
SD.rna.C<-apply(chimp.log2rna, 1,sd,na.rm = T)
mean.rna.C<-apply(chimp.log2rna, 1,mean,na.rm = T)

CV.rna.H <- SD.rna.H - mean.rna.H
CV.rna.R <- SD.rna.R - mean.rna.R
CV.rna.C <- SD.rna.C - mean.rna.C

TE

SD.TE.H<-apply(human.log2TE, 1,sd,na.rm = T)
mean.TE.H<-apply(human.log2TE, 1,mean,na.rm = T)
SD.TE.R<-apply(rhesus.log2TE, 1,sd,na.rm = T)
mean.TE.R<-apply(rhesus.log2TE, 1,mean,na.rm = T)
SD.TE.C<-apply(chimp.log2TE, 1,sd,na.rm = T)
mean.TE.C<-apply(chimp.log2TE, 1,mean,na.rm = T)

CV.TE.H <- SD.TE.H - mean.TE.H
CV.TE.R <- SD.TE.R - mean.TE.R
CV.TE.C <- SD.TE.C - mean.TE.C

make dataframe for ggplot

human.chimp.ribo.spearman<-as.vector(cor(human.log2ribo,chimp.log2ribo,use="complete.obs",method="spearman"))
human.rhesus.ribo.spearman<-as.vector(cor(rhesus.log2ribo,human.log2ribo,use="complete.obs",method="spearman"))
rhesus.chimp.ribo.spearman<-as.vector(cor(rhesus.log2ribo,chimp.log2ribo,use="complete.obs",method="spearman"))

human.chimp.rna.spearman<-as.vector(cor(human.log2rna,chimp.log2rna,use="complete.obs",method="spearman"))
human.rhesus.rna.spearman<-as.vector(cor(rhesus.log2rna,human.log2rna,use="complete.obs",method="spearman"))
rhesus.chimp.rna.spearman<-as.vector(cor(rhesus.log2rna,chimp.log2rna,use="complete.obs",method="spearman"))

human.chimp.TE.spearman<-as.vector(cor(human.log2TE,chimp.log2TE,use="complete.obs",method="spearman"))
human.rhesus.TE.spearman<-as.vector(cor(rhesus.log2TE,human.log2TE,use="complete.obs",method="spearman"))
rhesus.chimp.TE.spearman<-as.vector(cor(rhesus.log2TE,chimp.log2TE,use="complete.obs",method="spearman"))
cross.species.spearman<-as.data.frame(cbind(human.chimp.ribo.spearman,human.rhesus.ribo.spearman,rhesus.chimp.ribo.spearman,human.chimp.rna.spearman,human.rhesus.rna.spearman,rhesus.chimp.rna.spearman,human.chimp.TE.spearman,human.rhesus.TE.spearman,rhesus.chimp.TE.spearman))
cross.species.spearman<-melt(cross.species.spearman)
## No id variables; using all as measure variables

ribo

hist(cor(human.log2ribo,chimp.log2ribo,use="complete.obs",method="spearman"), col=rgb(0,0,1,0.5), xlab="Spearman's rho", main="Ribo" , xlim=c(0.6,1), ylim = c(0,15))
hist(cor(rhesus.log2ribo,chimp.log2ribo,use="complete.obs",method="spearman"), add =T, col=rgb(1,0,0,0.5))
hist(cor(rhesus.log2ribo,human.log2ribo,use="complete.obs",method="spearman"), add=T,col=rgb(0,1,0,0.5), breaks =2)
legend("topright", legend=c("Human vs. Chimp", "Rhesus vs. Chimp", "Rhesus vs. Human"), lwd=3,lty=1, col=c("blue","red","green"), cex=0.75, bty="n")

cross.species.spearman %>% filter(grepl(variable,pattern = "ribo")) %>% ggplot(aes(x=value)) + geom_density(aes(fill=variable), alpha=0.3)+labs(title="ribo spearman between species", x = "Spearman")+scale_fill_discrete(name="species pairs",labels =c("Human-Chimp","Human-Rhesus","Rhesus-Chimp"))

cross.species.spearman %>% filter(grepl(variable,pattern = "ribo")) %>% ggplot(aes(x=value)) + geom_histogram(aes(fill=variable), alpha=0.3, position="identity" )+labs(title="ribo spearman between species", x = "Spearman")+scale_fill_discrete(name="species pairs",labels =c("Human-Chimp","Human-Rhesus","Rhesus-Chimp"))
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.

rna

hist(cor(human.log2rna,chimp.log2rna,use="complete.obs",method="spearman"), col=rgb(0,0,1,0.5), xlab="Spearman's rho", main="rna" , xlim=c(0.6,1), ylim = c(0,20))
hist(cor(rhesus.log2rna,chimp.log2rna,use="complete.obs",method="spearman"), add =T, col=rgb(1,0,0,0.5),breaks =2)
hist(cor(rhesus.log2rna,human.log2rna,use="complete.obs",method="spearman"), add=T,col=rgb(0,1,0,0.5))
legend("topright", legend=c("Human vs. Chimp", "Rhesus vs. Chimp", "Rhesus vs. Human"), lwd=3,lty=1, col=c("blue","red","green"), cex=0.75, bty="n")

cross.species.spearman %>% filter(grepl(variable,pattern = "rna")) %>% ggplot(aes(x=value)) + geom_density(aes(fill=variable), alpha=0.3)+labs(title="rna spearman between species", x = "Spearman")+scale_fill_discrete(name="species pairs",labels =c("Human-Chimp","Human-Rhesus","Rhesus-Chimp"))

cross.species.spearman %>% filter(grepl(variable,pattern = "rna")) %>% ggplot(aes(x=value)) + geom_histogram(aes(fill=variable), alpha=0.3, position="identity")+labs(title="rna spearman between species", x = "Spearman")+scale_fill_discrete(name="species pairs",labels =c("Human-Chimp","Human-Rhesus","Rhesus-Chimp"))
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.

TE

hist(cor(human.log2TE,chimp.log2TE,use="complete.obs",method="spearman"), col=rgb(0,0,1,0.5), xlab="Spearman's rho", main="TE" , xlim=c(0.4,1), ylim = c(0,15))
hist(cor(rhesus.log2TE,chimp.log2TE,use="complete.obs",method="spearman"), add =T, col=rgb(1,0,0,0.5))
hist(cor(rhesus.log2TE,human.log2TE,use="complete.obs",method="spearman"), add=T,col=rgb(0,1,0,0.5))
legend("topright", legend=c("Human vs. Chimp", "Rhesus vs. Chimp", "Rhesus vs. Human"), lwd=3,lty=1, col=c("blue","red","green"), cex=0.75, bty="n")

cross.species.spearman %>% filter(grepl(variable,pattern = "TE")) %>% ggplot(aes(x=value)) + geom_density(aes(fill=variable), alpha=0.3)+labs(title="TE spearman between species", x = "Spearman")+scale_fill_discrete(name="species pairs",labels =c("Human-Chimp","Human-Rhesus","Rhesus-Chimp"))

cross.species.spearman %>% filter(grepl(variable,pattern = "TE")) %>% ggplot(aes(x=value)) + geom_histogram(aes(fill=variable), alpha=0.3, position="identity")+labs(title="TE spearman between species", x = "Spearman")+scale_fill_discrete(name="species pairs",labels =c("Human-Chimp","Human-Rhesus","Rhesus-Chimp"))
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.

seperate data into categories

by cv

ribo.mean.CV<-apply(cbind(CV.ribo.H,CV.ribo.R,CV.ribo.C),1,mean)

ribo.cv.cat <- cut(ribo.mean.CV,breaks = summary(ribo.mean.CV))                

rna.mean.CV<-apply(cbind(CV.rna.H,CV.rna.R,CV.rna.C),1,mean)
rna.cv.cat <- cut(rna.mean.CV,breaks = summary(rna.mean.CV))

TE.mean.CV<-apply(cbind(CV.TE.H,CV.TE.R,CV.TE.C),1,mean)
TE.cv.cat <- cut(TE.mean.CV,breaks = summary(TE.mean.CV))

#ribo
human.chimp.ribo.spearman<-matrix(nrow = 25, ncol = 5)

for (i in 1:5){

  human.chimp.ribo.spearman[,i]<-as.vector(cor(human.log2ribo[as.numeric(ribo.cv.cat) == i,],chimp.log2ribo[as.numeric(ribo.cv.cat) == i,],use="complete.obs",method="spearman"))
}                                           
  
human.chimp.ribo.spearman<-melt(human.chimp.ribo.spearman)

human.chimp.ribo.spearman$Var1<-c("human.chimp")
                            
human.rhesus.ribo.spearman<-matrix(nrow = 25, ncol = 5)

for (i in 1:5){
  
  human.rhesus.ribo.spearman[,i]<-as.vector(cor(human.log2ribo[as.numeric(ribo.cv.cat) == i,],rhesus.log2ribo[as.numeric(ribo.cv.cat) == i,],use="complete.obs",method="spearman"))
}                                           

human.rhesus.ribo.spearman<-melt(human.rhesus.ribo.spearman)

human.rhesus.ribo.spearman$Var1<-c("human.rhesus")

chimp.rhesus.ribo.spearman<-matrix(nrow = 25, ncol = 5)

for (i in 1:5){
  
  chimp.rhesus.ribo.spearman[,i]<-as.vector(cor(chimp.log2ribo[as.numeric(ribo.cv.cat) == i,],rhesus.log2ribo[as.numeric(ribo.cv.cat) == i,],use="complete.obs",method="spearman"))
}                                           

chimp.rhesus.ribo.spearman<-melt(chimp.rhesus.ribo.spearman)
chimp.rhesus.ribo.spearman$Var1<-c("chimp.rhesus")

HCR.ribo.spearman.by.cv<-as.data.frame(rbind(chimp.rhesus.ribo.spearman,human.rhesus.ribo.spearman,human.chimp.ribo.spearman))

HCR.ribo.spearman.by.cv %>% ggplot(aes(x=value)) + geom_density(aes(fill=Var1), alpha=0.3)+labs(title="ribo spearman between species", x = "Spearman")+scale_fill_discrete(name="species pairs")+facet_wrap(~Var2)

HCR.ribo.spearman.by.cv %>% ggplot(aes(x=value)) + geom_histogram(aes(fill=Var1), alpha=0.3, position="identity")+labs(title="ribo spearman between species", x = "Spearman")+scale_fill_discrete(name="species pairs")+facet_wrap(~Var2)
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.

#rna
human.chimp.rna.spearman<-matrix(nrow = 25, ncol = 5)

for (i in 1:5){
  
  human.chimp.rna.spearman[,i]<-as.vector(cor(human.log2rna[as.numeric(rna.cv.cat) == i,],chimp.log2rna[as.numeric(rna.cv.cat) == i,],use="complete.obs",method="spearman"))
}                                           

human.chimp.rna.spearman<-melt(human.chimp.rna.spearman)

human.chimp.rna.spearman$Var1<-c("human.chimp")

human.rhesus.rna.spearman<-matrix(nrow = 25, ncol = 5)

for (i in 1:5){
  
  human.rhesus.rna.spearman[,i]<-as.vector(cor(human.log2rna[as.numeric(rna.cv.cat) == i,],rhesus.log2rna[as.numeric(rna.cv.cat) == i,],use="complete.obs",method="spearman"))
}                                           

human.rhesus.rna.spearman<-melt(human.rhesus.rna.spearman)

human.rhesus.rna.spearman$Var1<-c("human.rhesus")

chimp.rhesus.rna.spearman<-matrix(nrow = 25, ncol = 5)

for (i in 1:5){
  
  chimp.rhesus.rna.spearman[,i]<-as.vector(cor(chimp.log2rna[as.numeric(rna.cv.cat) == i,],rhesus.log2rna[as.numeric(rna.cv.cat) == i,],use="complete.obs",method="spearman"))
}                                           

chimp.rhesus.rna.spearman<-melt(chimp.rhesus.rna.spearman)
chimp.rhesus.rna.spearman$Var1<-c("chimp.rhesus")

HCR.rna.spearman.by.cv<-as.data.frame(rbind(chimp.rhesus.rna.spearman,human.rhesus.rna.spearman,human.chimp.rna.spearman))

HCR.rna.spearman.by.cv %>% ggplot(aes(x=value)) + geom_density(aes(fill=Var1), alpha=0.3)+labs(title="rna spearman between species", x = "Spearman")+scale_fill_discrete(name="species pairs")+facet_wrap(~Var2)

HCR.rna.spearman.by.cv %>% ggplot(aes(x=value)) + geom_histogram(aes(fill=Var1), alpha=0.3, position="identity")+labs(title="rna spearman between species", x = "Spearman")+scale_fill_discrete(name="species pairs")+facet_wrap(~Var2)
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.

#TE
human.chimp.TE.spearman<-matrix(nrow = 25, ncol = 5)

for (i in 1:5){
  
  human.chimp.TE.spearman[,i]<-as.vector(cor(human.log2TE[as.numeric(TE.cv.cat) == i,],chimp.log2TE[as.numeric(TE.cv.cat) == i,],use="complete.obs",method="spearman"))
}                                           

human.chimp.TE.spearman<-melt(human.chimp.TE.spearman)

human.chimp.TE.spearman$Var1<-c("human.chimp")

human.rhesus.TE.spearman<-matrix(nrow = 25, ncol = 5)

for (i in 1:5){
  
  human.rhesus.TE.spearman[,i]<-as.vector(cor(human.log2TE[as.numeric(TE.cv.cat) == i,],rhesus.log2TE[as.numeric(TE.cv.cat) == i,],use="complete.obs",method="spearman"))
}                                           

human.rhesus.TE.spearman<-melt(human.rhesus.TE.spearman)

human.rhesus.TE.spearman$Var1<-c("human.rhesus")

chimp.rhesus.TE.spearman<-matrix(nrow = 25, ncol = 5)

for (i in 1:5){
  
  chimp.rhesus.TE.spearman[,i]<-as.vector(cor(chimp.log2TE[as.numeric(TE.cv.cat) == i,],rhesus.log2TE[as.numeric(TE.cv.cat) == i,],use="complete.obs",method="spearman"))
}                                           

chimp.rhesus.TE.spearman<-melt(chimp.rhesus.TE.spearman)
chimp.rhesus.TE.spearman$Var1<-c("chimp.rhesus")

HCR.TE.spearman.by.cv<-as.data.frame(rbind(chimp.rhesus.TE.spearman,human.rhesus.TE.spearman,human.chimp.TE.spearman))

HCR.TE.spearman.by.cv %>% ggplot(aes(x=value)) + geom_density(aes(fill=Var1), alpha=0.3)+labs(title="TE spearman between species", x = "Spearman")+scale_fill_discrete(name="species pairs")+facet_wrap(~Var2)

HCR.TE.spearman.by.cv %>% ggplot(aes(x=value)) + geom_histogram(aes(fill=Var1), alpha=0.3, position="identity")+labs(title="TE spearman between species", x = "Spearman")+scale_fill_discrete(name="species pairs")+facet_wrap(~Var2)
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.

by SD

ribo.mean.SD<-apply(cbind(SD.ribo.H,SD.ribo.R,SD.ribo.C),1,mean)

ribo.SD.cat <- cut(ribo.mean.SD,breaks = summary(ribo.mean.SD))                

rna.mean.SD<-apply(cbind(SD.rna.H,SD.rna.R,SD.rna.C),1,mean)
rna.SD.cat <- cut(rna.mean.SD,breaks = summary(rna.mean.SD))

TE.mean.SD<-apply(cbind(SD.TE.H,SD.TE.R,SD.TE.C),1,mean)
TE.SD.cat <- cut(TE.mean.SD,breaks = summary(TE.mean.SD))

#ribo
human.chimp.ribo.spearman<-matrix(nrow = 25, ncol = 5)

for (i in 1:5){
  
  human.chimp.ribo.spearman[,i]<-as.vector(cor(human.log2ribo[as.numeric(ribo.SD.cat) == i,],chimp.log2ribo[as.numeric(ribo.SD.cat) == i,],use="complete.obs",method="spearman"))
}                                           

human.chimp.ribo.spearman<-melt(human.chimp.ribo.spearman)

human.chimp.ribo.spearman$Var1<-c("human.chimp")

human.rhesus.ribo.spearman<-matrix(nrow = 25, ncol = 5)

for (i in 1:5){
  
  human.rhesus.ribo.spearman[,i]<-as.vector(cor(human.log2ribo[as.numeric(ribo.SD.cat) == i,],rhesus.log2ribo[as.numeric(ribo.SD.cat) == i,],use="complete.obs",method="spearman"))
}                                           

human.rhesus.ribo.spearman<-melt(human.rhesus.ribo.spearman)

human.rhesus.ribo.spearman$Var1<-c("human.rhesus")

chimp.rhesus.ribo.spearman<-matrix(nrow = 25, ncol = 5)

for (i in 1:5){
  
  chimp.rhesus.ribo.spearman[,i]<-as.vector(cor(chimp.log2ribo[as.numeric(ribo.SD.cat) == i,],rhesus.log2ribo[as.numeric(ribo.SD.cat) == i,],use="complete.obs",method="spearman"))
}                                           

chimp.rhesus.ribo.spearman<-melt(chimp.rhesus.ribo.spearman)
chimp.rhesus.ribo.spearman$Var1<-c("chimp.rhesus")

HCR.ribo.spearman.by.SD<-as.data.frame(rbind(chimp.rhesus.ribo.spearman,human.rhesus.ribo.spearman,human.chimp.ribo.spearman))

HCR.ribo.spearman.by.SD %>% ggplot(aes(x=value)) + geom_density(aes(fill=Var1), alpha=0.3)+labs(title="ribo spearman between species", x = "Spearman")+scale_fill_discrete(name="species pairs")+facet_wrap(~Var2)

HCR.ribo.spearman.by.SD %>% ggplot(aes(x=value)) + geom_histogram(aes(fill=Var1), alpha=0.3, position="identity")+labs(title="ribo spearman between species", x = "Spearman")+scale_fill_discrete(name="species pairs")+facet_wrap(~Var2)
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.

#rna
human.chimp.rna.spearman<-matrix(nrow = 25, ncol = 5)

for (i in 1:5){
  
  human.chimp.rna.spearman[,i]<-as.vector(cor(human.log2rna[as.numeric(rna.SD.cat) == i,],chimp.log2rna[as.numeric(rna.SD.cat) == i,],use="complete.obs",method="spearman"))
}                                           

human.chimp.rna.spearman<-melt(human.chimp.rna.spearman)

human.chimp.rna.spearman$Var1<-c("human.chimp")

human.rhesus.rna.spearman<-matrix(nrow = 25, ncol = 5)

for (i in 1:5){
  
  human.rhesus.rna.spearman[,i]<-as.vector(cor(human.log2rna[as.numeric(rna.SD.cat) == i,],rhesus.log2rna[as.numeric(rna.SD.cat) == i,],use="complete.obs",method="spearman"))
}                                           

human.rhesus.rna.spearman<-melt(human.rhesus.rna.spearman)

human.rhesus.rna.spearman$Var1<-c("human.rhesus")

chimp.rhesus.rna.spearman<-matrix(nrow = 25, ncol = 5)

for (i in 1:5){
  
  chimp.rhesus.rna.spearman[,i]<-as.vector(cor(chimp.log2rna[as.numeric(rna.SD.cat) == i,],rhesus.log2rna[as.numeric(rna.SD.cat) == i,],use="complete.obs",method="spearman"))
}                                           

chimp.rhesus.rna.spearman<-melt(chimp.rhesus.rna.spearman)
chimp.rhesus.rna.spearman$Var1<-c("chimp.rhesus")

HCR.rna.spearman.by.SD<-as.data.frame(rbind(chimp.rhesus.rna.spearman,human.rhesus.rna.spearman,human.chimp.rna.spearman))

HCR.rna.spearman.by.SD %>% ggplot(aes(x=value)) + geom_density(aes(fill=Var1), alpha=0.3)+labs(title="rna spearman between species", x = "Spearman")+scale_fill_discrete(name="species pairs")+facet_wrap(~Var2)

HCR.rna.spearman.by.SD %>% ggplot(aes(x=value)) + geom_histogram(aes(fill=Var1), alpha=0.3, position="identity")+labs(title="rna spearman between species", x = "Spearman")+scale_fill_discrete(name="species pairs")+facet_wrap(~Var2)
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.

#TE
human.chimp.TE.spearman<-matrix(nrow = 25, ncol = 5)

for (i in 1:5){
  
  human.chimp.TE.spearman[,i]<-as.vector(cor(human.log2TE[as.numeric(TE.SD.cat) == i,],chimp.log2TE[as.numeric(TE.SD.cat) == i,],use="complete.obs",method="spearman"))
}                                           

human.chimp.TE.spearman<-melt(human.chimp.TE.spearman)

human.chimp.TE.spearman$Var1<-c("human.chimp")

human.rhesus.TE.spearman<-matrix(nrow = 25, ncol = 5)

for (i in 1:5){
  
  human.rhesus.TE.spearman[,i]<-as.vector(cor(human.log2TE[as.numeric(TE.SD.cat) == i,],rhesus.log2TE[as.numeric(TE.SD.cat) == i,],use="complete.obs",method="spearman"))
}                                           

human.rhesus.TE.spearman<-melt(human.rhesus.TE.spearman)

human.rhesus.TE.spearman$Var1<-c("human.rhesus")

chimp.rhesus.TE.spearman<-matrix(nrow = 25, ncol = 5)

for (i in 1:5){
  
  chimp.rhesus.TE.spearman[,i]<-as.vector(cor(chimp.log2TE[as.numeric(TE.SD.cat) == i,],rhesus.log2TE[as.numeric(TE.SD.cat) == i,],use="complete.obs",method="spearman"))
}                                           

chimp.rhesus.TE.spearman<-melt(chimp.rhesus.TE.spearman)
chimp.rhesus.TE.spearman$Var1<-c("chimp.rhesus")

HCR.TE.spearman.by.SD<-as.data.frame(rbind(chimp.rhesus.TE.spearman,human.rhesus.TE.spearman,human.chimp.TE.spearman))

HCR.TE.spearman.by.SD %>% ggplot(aes(x=value)) + geom_density(aes(fill=Var1), alpha=0.3)+labs(title="TE spearman between species", x = "Spearman")+scale_fill_discrete(name="species pairs")+facet_wrap(~Var2)

HCR.TE.spearman.by.SD %>% ggplot(aes(x=value)) + geom_histogram(aes(fill=Var1), alpha=0.3, position="identity")+labs(title="TE spearman between species", x = "Spearman")+scale_fill_discrete(name="species pairs")+facet_wrap(~Var2)
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.

by SD between/ave SD within

log2ribo<-exprs(eSetRRP.RP.Q.log2[,eSetRRP.RP.Q.log2$seqData == "ribo"])
log2rna<-exprs(eSetRRP.RP.Q.log2[,eSetRRP.RP.Q.log2$seqData == "rna"])
log2TE <- log2ribo-log2rna

SD.ribo<-apply(log2ribo, 1,sd,na.rm = T)

SD.rna<-apply(log2rna, 1,sd,na.rm = T)

SD.TE<-apply(log2TE, 1,sd,na.rm = T)


ribo.mean.SD<-apply(cbind(SD.ribo.H,SD.ribo.R,SD.ribo.C),1,mean)

ribo.SD.cat <- cut(SD.ribo-ribo.mean.SD,breaks = quantile(SD.ribo-ribo.mean.SD,probs = seq(0, 1, 0.1)))                
             

rna.mean.SD<-apply(cbind(SD.rna.H,SD.rna.R,SD.rna.C),1,mean)

rna.SD.cat <- cut(SD.rna-rna.mean.SD,breaks = quantile(SD.rna-rna.mean.SD,probs = seq(0, 1, 0.1)))                

TE.mean.SD<-apply(cbind(SD.TE.H,SD.TE.R,SD.TE.C),1,mean)
TE.SD.cat <- cut(SD.TE-TE.mean.SD,breaks = quantile(SD.TE-TE.mean.SD,probs = seq(0, 1, 0.1)))                

#ribo

#ribo
human.chimp.ribo.spearman<-matrix(nrow = 25, ncol = 10)

for (i in 1:10){
  
  human.chimp.ribo.spearman[,i]<-as.vector(cor(human.log2ribo[as.numeric(ribo.SD.cat) == i,],chimp.log2ribo[as.numeric(ribo.SD.cat) == i,],use="complete.obs",method="spearman"))
}                                           

human.chimp.ribo.spearman<-melt(human.chimp.ribo.spearman)

human.chimp.ribo.spearman$Var1<-c("human.chimp")

human.rhesus.ribo.spearman<-matrix(nrow = 25, ncol = 10)

for (i in 1:10){
  
  human.rhesus.ribo.spearman[,i]<-as.vector(cor(human.log2ribo[as.numeric(ribo.SD.cat) == i,],rhesus.log2ribo[as.numeric(ribo.SD.cat) == i,],use="complete.obs",method="spearman"))
}                                           

human.rhesus.ribo.spearman<-melt(human.rhesus.ribo.spearman)

human.rhesus.ribo.spearman$Var1<-c("human.rhesus")

chimp.rhesus.ribo.spearman<-matrix(nrow = 25, ncol = 10)

for (i in 1:10){
  
  chimp.rhesus.ribo.spearman[,i]<-as.vector(cor(chimp.log2ribo[as.numeric(ribo.SD.cat) == i,],rhesus.log2ribo[as.numeric(ribo.SD.cat) == i,],use="complete.obs",method="spearman"))
}                                           

chimp.rhesus.ribo.spearman<-melt(chimp.rhesus.ribo.spearman)
chimp.rhesus.ribo.spearman$Var1<-c("chimp.rhesus")

HCR.ribo.spearman.by.SD<-as.data.frame(rbind(chimp.rhesus.ribo.spearman,human.rhesus.ribo.spearman,human.chimp.ribo.spearman))

HCR.ribo.spearman.by.SD %>% ggplot(aes(x=value)) + geom_density(aes(fill=Var1), alpha=0.3)+labs(title="ribo spearman between species", x = "Spearman")+scale_fill_discrete(name="species pairs")+facet_wrap(~Var2)

HCR.ribo.spearman.by.SD %>% ggplot(aes(x=value)) + geom_histogram(aes(fill=Var1), alpha=0.3, position="identity")+labs(title="ribo spearman between species", x = "Spearman")+scale_fill_discrete(name="species pairs")+facet_wrap(~Var2)
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.

#rna
human.chimp.rna.spearman<-matrix(nrow = 25, ncol = 10)

for (i in 1:10){
  
  human.chimp.rna.spearman[,i]<-as.vector(cor(human.log2rna[as.numeric(rna.SD.cat) == i,],chimp.log2rna[as.numeric(rna.SD.cat) == i,],use="complete.obs",method="spearman"))
}                                           

human.chimp.rna.spearman<-melt(human.chimp.rna.spearman)

human.chimp.rna.spearman$Var1<-c("human.chimp")

human.rhesus.rna.spearman<-matrix(nrow = 25, ncol = 10)

for (i in 1:10){
  
  human.rhesus.rna.spearman[,i]<-as.vector(cor(human.log2rna[as.numeric(rna.SD.cat) == i,],rhesus.log2rna[as.numeric(rna.SD.cat) == i,],use="complete.obs",method="spearman"))
}                                           

human.rhesus.rna.spearman<-melt(human.rhesus.rna.spearman)

human.rhesus.rna.spearman$Var1<-c("human.rhesus")

chimp.rhesus.rna.spearman<-matrix(nrow = 25, ncol = 10)

for (i in 1:10){
  
  chimp.rhesus.rna.spearman[,i]<-as.vector(cor(chimp.log2rna[as.numeric(rna.SD.cat) == i,],rhesus.log2rna[as.numeric(rna.SD.cat) == i,],use="complete.obs",method="spearman"))
}                                           

chimp.rhesus.rna.spearman<-melt(chimp.rhesus.rna.spearman)
chimp.rhesus.rna.spearman$Var1<-c("chimp.rhesus")

HCR.rna.spearman.by.SD<-as.data.frame(rbind(chimp.rhesus.rna.spearman,human.rhesus.rna.spearman,human.chimp.rna.spearman))

HCR.rna.spearman.by.SD %>% ggplot(aes(x=value)) + geom_density(aes(fill=Var1), alpha=0.3)+labs(title="rna spearman between species", x = "Spearman")+scale_fill_discrete(name="species pairs")+facet_wrap(~Var2)

HCR.rna.spearman.by.SD %>% ggplot(aes(x=value)) + geom_histogram(aes(fill=Var1), alpha=0.3, position="identity")+labs(title="rna spearman between species", x = "Spearman")+scale_fill_discrete(name="species pairs")+facet_wrap(~Var2)
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.

#TE
human.chimp.TE.spearman<-matrix(nrow = 25, ncol = 10)

for (i in 1:10){
  
  human.chimp.TE.spearman[,i]<-as.vector(cor(human.log2TE[as.numeric(TE.SD.cat) == i,],chimp.log2TE[as.numeric(TE.SD.cat) == i,],use="complete.obs",method="spearman"))
}                                           

human.chimp.TE.spearman<-melt(human.chimp.TE.spearman)

human.chimp.TE.spearman$Var1<-c("human.chimp")

human.rhesus.TE.spearman<-matrix(nrow = 25, ncol = 10)

for (i in 1:10){
  
  human.rhesus.TE.spearman[,i]<-as.vector(cor(human.log2TE[as.numeric(TE.SD.cat) == i,],rhesus.log2TE[as.numeric(TE.SD.cat) == i,],use="complete.obs",method="spearman"))
}                                           

human.rhesus.TE.spearman<-melt(human.rhesus.TE.spearman)

human.rhesus.TE.spearman$Var1<-c("human.rhesus")

chimp.rhesus.TE.spearman<-matrix(nrow = 25, ncol = 10)

for (i in 1:10){
  
  chimp.rhesus.TE.spearman[,i]<-as.vector(cor(chimp.log2TE[as.numeric(TE.SD.cat) == i,],rhesus.log2TE[as.numeric(TE.SD.cat) == i,],use="complete.obs",method="spearman"))
}                                           

chimp.rhesus.TE.spearman<-melt(chimp.rhesus.TE.spearman)
chimp.rhesus.TE.spearman$Var1<-c("chimp.rhesus")

HCR.TE.spearman.by.SD<-as.data.frame(rbind(chimp.rhesus.TE.spearman,human.rhesus.TE.spearman,human.chimp.TE.spearman))

HCR.TE.spearman.by.SD %>% ggplot(aes(x=value)) + geom_density(aes(fill=Var1), alpha=0.3)+labs(title="TE spearman between species", x = "Spearman")+scale_fill_discrete(name="species pairs")+facet_wrap(~Var2)

HCR.TE.spearman.by.SD %>% ggplot(aes(x=value)) + geom_histogram(aes(fill=Var1), alpha=0.3, position="identity")+labs(title="TE spearman between species", x = "Spearman")+scale_fill_discrete(name="species pairs")+facet_wrap(~Var2)
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.

by mean

log2ribo<-exprs(eSetRRP.RP.Q.log2[,eSetRRP.RP.Q.log2$seqData == "ribo"])
log2rna<-exprs(eSetRRP.RP.Q.log2[,eSetRRP.RP.Q.log2$seqData == "rna"])
log2TE <- log2ribo-log2rna

mean.ribo<-apply(log2ribo, 1,mean,na.rm = T)

mean.rna<-apply(log2rna, 1,mean,na.rm = T)

mean.TE<-apply(log2TE, 1,mean,na.rm = T)

#


ribo.mean.cat <- cut(mean.ribo,breaks = summary(mean.ribo))                

rna.mean.cat <- cut(mean.rna,breaks = summary(mean.rna))

TE.mean.cat <- cut(mean.TE,breaks = summary(mean.TE))

#ribo
human.chimp.ribo.spearman<-matrix(nrow = 25, ncol = 5)

for (i in 1:5){
  
  human.chimp.ribo.spearman[,i]<-as.vector(cor(human.log2ribo[as.numeric(ribo.mean.cat) == i,],chimp.log2ribo[as.numeric(ribo.mean.cat) == i,],use="complete.obs",method="spearman"))
}                                           

human.chimp.ribo.spearman<-melt(human.chimp.ribo.spearman)

human.chimp.ribo.spearman$Var1<-c("human.chimp")

human.rhesus.ribo.spearman<-matrix(nrow = 25, ncol = 5)

for (i in 1:5){
  
  human.rhesus.ribo.spearman[,i]<-as.vector(cor(human.log2ribo[as.numeric(ribo.mean.cat) == i,],rhesus.log2ribo[as.numeric(ribo.mean.cat) == i,],use="complete.obs",method="spearman"))
}                                           

human.rhesus.ribo.spearman<-melt(human.rhesus.ribo.spearman)

human.rhesus.ribo.spearman$Var1<-c("human.rhesus")

chimp.rhesus.ribo.spearman<-matrix(nrow = 25, ncol = 5)

for (i in 1:5){
  
  chimp.rhesus.ribo.spearman[,i]<-as.vector(cor(chimp.log2ribo[as.numeric(ribo.mean.cat) == i,],rhesus.log2ribo[as.numeric(ribo.mean.cat) == i,],use="complete.obs",method="spearman"))
}                                           

chimp.rhesus.ribo.spearman<-melt(chimp.rhesus.ribo.spearman)
chimp.rhesus.ribo.spearman$Var1<-c("chimp.rhesus")

HCR.ribo.spearman.by.SD<-as.data.frame(rbind(chimp.rhesus.ribo.spearman,human.rhesus.ribo.spearman,human.chimp.ribo.spearman))

HCR.ribo.spearman.by.SD %>% ggplot(aes(x=value)) + geom_density(aes(fill=Var1), alpha=0.3)+labs(title="ribo spearman between species", x = "Spearman")+scale_fill_discrete(name="species pairs")+facet_wrap(~Var2)

HCR.ribo.spearman.by.SD %>% ggplot(aes(x=value)) + geom_histogram(aes(fill=Var1), alpha=0.3, position="identity")+labs(title="ribo spearman between species", x = "Spearman")+scale_fill_discrete(name="species pairs")+facet_wrap(~Var2)
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.

#rna
human.chimp.rna.spearman<-matrix(nrow = 25, ncol = 5)

for (i in 1:5){
  
  human.chimp.rna.spearman[,i]<-as.vector(cor(human.log2rna[as.numeric(rna.mean.cat) == i,],chimp.log2rna[as.numeric(rna.mean.cat) == i,],use="complete.obs",method="spearman"))
}                                           

human.chimp.rna.spearman<-melt(human.chimp.rna.spearman)

human.chimp.rna.spearman$Var1<-c("human.chimp")

human.rhesus.rna.spearman<-matrix(nrow = 25, ncol = 5)

for (i in 1:5){
  
  human.rhesus.rna.spearman[,i]<-as.vector(cor(human.log2rna[as.numeric(rna.mean.cat) == i,],rhesus.log2rna[as.numeric(rna.mean.cat) == i,],use="complete.obs",method="spearman"))
}                                           

human.rhesus.rna.spearman<-melt(human.rhesus.rna.spearman)

human.rhesus.rna.spearman$Var1<-c("human.rhesus")

chimp.rhesus.rna.spearman<-matrix(nrow = 25, ncol = 5)

for (i in 1:5){
  
  chimp.rhesus.rna.spearman[,i]<-as.vector(cor(chimp.log2rna[as.numeric(rna.mean.cat) == i,],rhesus.log2rna[as.numeric(rna.mean.cat) == i,],use="complete.obs",method="spearman"))
}                                           

chimp.rhesus.rna.spearman<-melt(chimp.rhesus.rna.spearman)
chimp.rhesus.rna.spearman$Var1<-c("chimp.rhesus")

HCR.rna.spearman.by.SD<-as.data.frame(rbind(chimp.rhesus.rna.spearman,human.rhesus.rna.spearman,human.chimp.rna.spearman))

HCR.rna.spearman.by.SD %>% ggplot(aes(x=value)) + geom_density(aes(fill=Var1), alpha=0.3)+labs(title="rna spearman between species", x = "Spearman")+scale_fill_discrete(name="species pairs")+facet_wrap(~Var2)

HCR.rna.spearman.by.SD %>% ggplot(aes(x=value)) + geom_histogram(aes(fill=Var1), alpha=0.3, position="identity")+labs(title="rna spearman between species", x = "Spearman")+scale_fill_discrete(name="species pairs")+facet_wrap(~Var2)
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.

#TE
human.chimp.TE.spearman<-matrix(nrow = 25, ncol = 5)

for (i in 1:5){
  
  human.chimp.TE.spearman[,i]<-as.vector(cor(human.log2TE[as.numeric(TE.mean.cat) == i,],chimp.log2TE[as.numeric(TE.mean.cat) == i,],use="complete.obs",method="spearman"))
}                                           

human.chimp.TE.spearman<-melt(human.chimp.TE.spearman)

human.chimp.TE.spearman$Var1<-c("human.chimp")

human.rhesus.TE.spearman<-matrix(nrow = 25, ncol = 5)

for (i in 1:5){
  
  human.rhesus.TE.spearman[,i]<-as.vector(cor(human.log2TE[as.numeric(TE.mean.cat) == i,],rhesus.log2TE[as.numeric(TE.mean.cat) == i,],use="complete.obs",method="spearman"))
}                                           

human.rhesus.TE.spearman<-melt(human.rhesus.TE.spearman)

human.rhesus.TE.spearman$Var1<-c("human.rhesus")

chimp.rhesus.TE.spearman<-matrix(nrow = 25, ncol = 5)

for (i in 1:5){
  
  chimp.rhesus.TE.spearman[,i]<-as.vector(cor(chimp.log2TE[as.numeric(TE.mean.cat) == i,],rhesus.log2TE[as.numeric(TE.mean.cat) == i,],use="complete.obs",method="spearman"))
}                                           

chimp.rhesus.TE.spearman<-melt(chimp.rhesus.TE.spearman)
chimp.rhesus.TE.spearman$Var1<-c("chimp.rhesus")

HCR.TE.spearman.by.SD<-as.data.frame(rbind(chimp.rhesus.TE.spearman,human.rhesus.TE.spearman,human.chimp.TE.spearman))

HCR.TE.spearman.by.SD %>% ggplot(aes(x=value)) + geom_density(aes(fill=Var1), alpha=0.3)+labs(title="TE spearman between species", x = "Spearman")+scale_fill_discrete(name="species pairs")+facet_wrap(~Var2)

HCR.TE.spearman.by.SD %>% ggplot(aes(x=value)) + geom_histogram(aes(fill=Var1), alpha=0.3, position="identity")+labs(title="TE spearman between species", x = "Spearman")+scale_fill_discrete(name="species pairs")+facet_wrap(~Var2)
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.